OpenRaiser/Intern-Atlas huggingface dataset:
COMTEST Project: A Complete Modular Test Stand for Human and Humanoid Posture Control and Balance
Venue: arXiv cs.RO 2021
Year: 2021

This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantitative measures of performance. In order to provide versatility, the design of the system is oriented to modularity: all its components can be replaced or extended according to experimental needs, adding additional perturbation profiles, new evaluation principles, and alternative tracking systems. It will be possible to use the system with different kinds of robots and exoskeletons as well as for human experiments aimed at gaining insights into human balance capabilities.
Is research with qualitative data more prevalent and impactful now? Interviews, case studies, focus groups and ethnographies
Venue: arXiv cs.DL 2021
Year: 2021

Researchers, editors, educators and publishers need to understand the mix of research methods used in their field to guide decision making, with a current concern being that qualitative research is threatened by big data. Although there have been many studies of the prevalence of different methods within individual narrow fields, there have been no systematic studies across academia. In response, this article assesses the prevalence and citation impact of academic research 1996-2019 that reports one of four common methods to gather qualitative data: interviews; focus groups; case studies; ethnography. The results show that, with minor exceptions, the prevalence of qualitative data has increased, often substantially, since 1996. In addition, all 27 broad fields (as classified by Scopus) now publish some qualitative research, with interviewing being by far the most common approach. This suggest that qualitative methods teaching and should increase, and researchers, editors and publishers should be increasingly open to the value that qualitative data can bring.
Improving the filtering of Branch-And-Bound MDD solver (extended)
Venue: arXiv cs.AI 2021
Year: 2021

This paper presents and evaluates two pruning techniques to reinforce the efficiency of constraint optimization solvers based on multi-valued decision-diagrams (MDD). It adopts the branch-and-bound framework proposed by Bergman et al. in 2016 to solve dynamic programs to optimality. In particular, our paper presents and evaluates the effectiveness of the local-bound (LocB) and rough upper-bound pruning (RUB). LocB is a new and effective rule that leverages the approximate MDD structure to avoid the exploration of non-interesting nodes. RUB is a rule to reduce the search space during the development of bounded-width-MDDs. The experimental study we conducted on the Maximum Independent Set Problem (MISP), Maximum Cut Problem (MCP), Maximum 2 Satisfiability (MAX2SAT) and the Traveling Salesman Problem with Time Windows (TSPTW) shows evidence indicating that rough-upper-bound and local-bound pruning have a high impact on optimization solvers based on branch-and-bound with MDDs. In particular, it shows that RUB delivers excellent results but requires some effort when defining the model. Also, it shows that LocB provides a significant improvement automatically; without necessitating any user-supplied information. Finally, it also shows that rough-upper-bound and local-bound pruning are not mutually exclusive, and their combined benefit supersedes the individual benefit of using each technique.
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning
Venue: arXiv cs.LG 2021
Year: 2021

Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predictions towards normal data. In this paper, we present a new supervised anomaly detector through introducing the novel Ensemble Active Learning Generative Adversarial Network (EAL-GAN). EAL-GAN is a conditional GAN having a unique one generator vs. multiple discriminators architecture where anomaly detection is implemented by an auxiliary classifier of the discriminator. In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data. We present extensive experimental results to demonstrate that the new anomaly detector consistently outperforms a variety of SOTA methods by significant margins. The codes are available on Github.
MultiCruise: Eco-Lane Selection Strategy with Eco-Cruise Control for Connected and Automated Vehicles
Venue: arXiv cs.RO 2021
Year: 2021

Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-selection strategy named MultiCruise, where each CAV selects one among multiple candidate lanes that allows the most energy-efficient travel. MultiCruise incorporates an Eco-Cruise component to select the most energy-efficient lane. The Eco-Cruise component calculates the driving parameters and prospective energy consumption of the ego vehicle for each candidate lane, and the Eco-Lane Selection component uses these values. As a result, MultiCruise can account for multiple data sources, such as the road curvature and the surrounding vehicles' velocities and accelerations. The eco-autonomous driving strategy, MultiCruise, is tested, designed and verified by using a co-simulation test platform that includes autonomous driving software and realistic road networks to study the performance under realistic driving conditions. Our experimental evaluations show that our eco-autonomous MultiCruise saves up to 8.5% fuel consumption.
Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data
Venue: arXiv cs.CY 2021
Year: 2021

Life pattern clustering is essential for abstracting the groups' characteristics of daily mobility patterns and activity regularity. Based on millions of GPS records, this paper proposed a framework on the life pattern clustering which can efficiently identify the groups have similar life pattern. The proposed method can retain original features of individual life pattern data without aggregation. Metagraph-based data structure is proposed for presenting the diverse life pattern. Spatial-temporal similarity includes significant places semantics, time sequential properties and frequency are integrated into this data structure, which captures the uncertainty of an individual and the diversities between individuals. Non-negative-factorization-based method was utilized for reducing the dimension. The results show that our proposed method can effectively identify the groups have similar life pattern and takes advantages in computation efficiency and robustness comparing with the traditional method. We revealed the representative life pattern groups and analyzed the group characteristics of human life patterns during different periods and different regions. We believe our work will help in future infrastructure planning, services improvement and policies making related to urban and transportation, thus promoting a humanized and sustainable city.
Measuring Novelty in Autonomous Vehicles Motion Using Local Outlier Factor Algorithm
Venue: arXiv cs.LG 2021
Year: 2021

Under unexpected conditions or scenarios, autonomous vehicles (AV) are more likely to follow abnormal unplanned actions, due to the limited set of rules or amount of experience they possess at that time. Enabling AV to measure the degree at which their movements are novel in real-time may help to decrease any possible negative consequences. We propose a method based on the Local Outlier Factor (LOF) algorithm to quantify this novelty measure. We extracted features from the inertial measurement unit (IMU) sensor's readings, which captures the vehicle's motion. We followed a novelty detection approach in which the model is fitted only using the normal data. Using datasets obtained from real-world vehicle missions, we demonstrate that the suggested metric can quantify to some extent the degree of novelty. Finally, a performance evaluation of the model confirms that our novelty metric can be practical.
Parikh's theorem for infinite alphabets
Venue: arXiv cs.FL 2021
Year: 2021

We investigate commutative images of languages recognised by register automata and grammars. Semi-linear and rational sets can be naturally extended to this setting by allowing for orbit-finite unions instead of only finite ones. We prove that commutative images of languages of one-register automata are not always semi-linear, but they are always rational. We also lift the latter result to grammars: commutative images of one-register context-free languages are rational, and in consequence commutatively equivalent to register automata. We conjecture analogous results for automata and grammars with arbitrarily many registers.
3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head
Venue: arXiv cs.CV 2021
Year: 2021

Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved. This is due to the lack of 3D generative models and available 3D emotional dataset with synchronized audios. To address this, we introduce 3D-TalkEmo, a deep neural network that generates 3D talking head animation with various emotions. We also create a large 3D dataset with synchronized audios and videos, rich corpus, as well as various emotion states of different persons with the sophisticated 3D face reconstruction methods. In the emotion generation network, we propose a novel 3D face representation structure - geometry map by classical multi-dimensional scaling analysis. It maps the coordinates of vertices on a 3D face to a canonical image plane, while preserving the vertex-to-vertex geodesic distance metric in a least-square sense. This maintains the adjacency relationship of each vertex and holds the effective convolutional structure for the 3D facial surface. Taking a neutral 3D mesh and a speech signal as inputs, the 3D-TalkEmo is able to generate vivid facial animations. Moreover, it provides access to change the emotion state of the animated speaker. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating the generated talking-heads of significantly higher quality compared to previous state-of-the-art methods.
Labeling Multipath via Reconfigurable Intelligent Surface
Venue: arXiv cs.IT 2021
Year: 2021

Reconfigurable intelligent surface (RIS) has shown promise in providing apparent benefits in wireless communication and positioning. Most of the existing research focuses on the ability of RIS to adjust the direction of propagation. In this paper, we present another application based on RIS, named multipath labeling, which intends to inject a label on propagation paths through the RISs. Each labeled path contains spatial knowledge between the RIS and the receiver, thus opening the door for sensing the surrounding world by RISs. The critical challenge is how the labeled paths can be extracted and distinguish from other paths, especially with multipath effects. To address this challenge, we present a complete labeling procedure consisting of channel flipping, parameter extraction, and RIS association. Simulations under a practice ray-tracing model reveal the feasibility of the proposed labeling technique even under the sub 6GHz channels with severe multipath effect. We also apply the multipath labeling technique to localization, which assists user equipment in obtaining its precise location through only a single base station.
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
Venue: arXiv cs.CV 2021
Year: 2021

3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regression. However, frame-by-frame 3D object detection suffers from noise, field-of-view obstruction, and sparsity. We propose a novel Temporal Fusion Module (TFM) to use information from previous time-steps to mitigate these problems. First, a state-of-the-art frustum network extracts point cloud features from raw RGB and LiDAR point cloud data frame-by-frame. Then, our TFM module fuses these features with a recurrent neural network. As a result, 3D object detection becomes robust against single frame failures and transient occlusions. Experiments on the KITTI object tracking dataset show the efficiency of the proposed TFM, where we obtain ~6%, ~4%, and ~6% improvements on Car, Pedestrian, and Cyclist classes, respectively, compared to frame-by-frame baselines. Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline. Our code is open-source and available at https://github.com/emecercelik/Temp-Frustum-Net.git.
Fair-Capacitated Clustering
Venue: arXiv cs.LG 2021
Year: 2021

Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a fair-representation of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make the clusters useful for the end-user, a balanced cardinality among the clusters is required. Our motivation comes from the education domain where studies indicate that students might learn better in diverse student groups and of course groups of similar cardinality are more practical e.g., for group assignments. To this end, we introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: i) we rely on fairlets to generate minimal sets that satisfy the fair constraint and ii) we propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering. The hierarchical approach embeds the additional cardinality requirements during the merging step while the partitioning-based one alters the assignment step using a knapsack problem formulation to satisfy the additional requirements. Our experiments on four educational datasets show that our approaches deliver well-balanced clusters in terms of both fairness and cardinality while maintaining a good clustering quality.
Mutant Density: A Measure of Fault-Sensitive Complexity
Venue: arXiv cs.SE 2021
Year: 2021

Software code complexity is a well-studied property to determine software component health. However, the existing code complexity metrics do not directly take into account the fault-proneness aspect of the code. We propose a metric called mutant density where we use mutation as a method to introduce artificial faults in code, and count the number of possible mutations per line. We show how this metric can be used to perform helpful analysis of real-life software projects.
Parallel mesh reconstruction streams for pose estimation of interacting hands
Venue: arXiv cs.CV 2021
Year: 2021

We present a new multi-stream 3D mesh reconstruction network (MSMR-Net) for hand pose estimation from a single RGB image. Our model consists of an image encoder followed by a mesh-convolution decoder composed of connected graph convolution layers. In contrast to previous models that form a single mesh decoding path, our decoder network incorporates multiple cross-resolution trajectories that are executed in parallel. Thus, global and local information are shared to form rich decoding representations at minor additional parameter cost compared to the single trajectory network. We demonstrate the effectiveness of our method in hand-hand and hand-object interaction scenarios at various levels of interaction. To evaluate the former scenario, we propose a method to generate RGB images of closely interacting hands. Moreoever, we suggest a metric to quantify the degree of interaction and show that close hand interactions are particularly challenging. Experimental results show that the MSMR-Net outperforms existing algorithms on the hand-object FreiHAND dataset as well as on our own hand-hand dataset.
Learning Aided Auctioning based Spectrum Access System in a Wireless Optical Network
Venue: arXiv cs.NI 2021
Year: 2021

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution (LTE) based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.
Power of Finite Memory and Finite Communication Robots under Asynchronous Scheduler
Venue: arXiv cs.DC 2021
Year: 2021

In swarm robotics, a set of robots has to perform a given task with specified internal capabilities (model) and under a given adversarial scheduler. Relation between a model $M_1$ under scheduler $S_1$, and that of a model $M_2$ under scheduler $S_2$ can be of four different types: not less powerful, more powerful, equivalent and orthogonal. In literature there are four main models of robots with lights: $\mathcal{LUMI}$, where robots have the power of observing the lights of all the robots, $\mathcal{FSTA}$ , where each robot can see only its own light, $\mathcal{FCOM}$, where each robot can observe the light of all other robots except its own and $\mathcal{OBLOT}$, where the robots do not have any light. In this paper, we investigate the computational power of $\mathcal{FSTA}$ and $\mathcal{FCOM}$ model under asynchronous scheduler by comparing it with other model and scheduler combinations. Our main focus is to understand and compare the power of persistent memory and explicit communication in robots under asynchronous scheduler.
RP-DQN: An application of Q-Learning to Vehicle Routing Problems
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods. We enable this by training from temporal differences. Specifically Q-Learning is employed. We show that our approach achieves state-of-the-art performance for autoregressive policies that sequentially insert nodes to construct solutions on the CVRP. Additionally, we are the first to tackle the MDVRP with machine learning methods and demonstrate that this problem type greatly benefits from our approach over other ML methods.
Single Stage Class Agnostic Common Object Detection: A Simple Baseline
Venue: arXiv cs.CV 2021
Year: 2021

This paper addresses the problem of common object detection, which aims to detect objects of similar categories from a set of images. Although it shares some similarities with the standard object detection and co-segmentation, common object detection, recently promoted by \cite{Jiang2019a}, has some unique advantages and challenges. First, it is designed to work on both closed-set and open-set conditions, a.k.a. known and unknown objects. Second, it must be able to match objects of the same category but not restricted to the same instance, texture, or posture. Third, it can distinguish multiple objects. In this work, we introduce the Single Stage Common Object Detection (SSCOD) to detect class-agnostic common objects from an image set. The proposed method is built upon the standard single-stage object detector. Furthermore, an embedded branch is introduced to generate the object's representation feature, and their similarity is measured by cosine distance. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. While being simple and flexible, our proposed SSCOD built upon ATSSNet performs significantly better than the baseline of the standard object detection, while still be able to match objects of unknown categories. Our source code can be found at \href{https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection}{(URL)}
Efficiently Partitioning the Edges of a 1-Planar Graph into a Planar Graph and a Forest
Venue: arXiv cs.DS 2021
Year: 2021

1-planar graphs are graphs that can be drawn in the plane such that any edge intersects with at most one other edge. Ackerman showed that the edges of a 1-planar graph can be partitioned into a planar graph and a forest, and claims that the proof leads to a linear time algorithm. However, it is not clear how one would obtain such an algorithm from his proof. In this paper, we first reprove Ackerman's result (in fact, we prove a slightly more general statement) and then show that the split can be found in linear time by using an edge-contraction data structure by Holm et al.
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model architectures were compared, and experimental results show that memory-augmented deep convolutional autoencoders perform the best at detecting out-of-distribution objects.